Ashwin Srinath
Ashwin Srinath is a senior software engineer at NVIDIA, and part of the team developing RAPIDS. Prior to joining NVIDIA, he was a computational scientist at Clemson University, helping researchers develop and optimize HPC applications.
Sessions
Pandas is loved and venerated for its flexibility and ease-of-use. However, its oft-quoted slowness has prompted many others like duckdb, polars, and RAPIDS cuDF to step in and offer faster alternatives. These are all fantastic tools, but they have non-zero adoption costs, more restrictive APIs compared to pandas, and they don’t always work with 3rd party libraries that use pandas today.
cudf.pandas takes a fresh approach: instead of trying to be a replacement for pandas, it effectively accelerates pandas on the GPU. cudf.pandas requires no code changes (not even your pandas imports!), supports 100% of the pandas API, and third-party libraries that use pandas are magically accelerated on the GPU.
If you use pandas today and want to run your code on the GPU with 0 changes today, this talk is for you. If you are the maintainer of a library that uses pandas and you’d like to support GPUs with 0 changes today, this talk is for you. If you’re a Pythonista at heart and enjoy hearing about the proxy pattern and deep import customization, this talk is for you!